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Estimating Contraceptive Prevalence Using Logistics Data for Short-Acting Methods: Analysis Across 30 Countries
Marc Cunningham1, Ariella Bock2, Niquelle Brown3
1John Snow, Inc., Arlington, VA, USA.
Global Health, Science and Practice
|September 17, 2015
Summary
Public sector contraceptive logistics data accurately estimate contraceptive prevalence rates when survey data are limited. Regression models offer the most accuracy for family planning program monitoring.
Area of Science:
- Public Health
- Demography
- Health Services Research
Background:
- Contraceptive prevalence rate (CPR) is crucial for family planning program evaluation.
- Current surveys provide infrequent CPR estimates, necessitating alternative methods.
- Contraceptive logistics data offer a potential source for more frequent CPR estimation.
Purpose of the Study:
- To develop and evaluate models for estimating public-sector contraceptive prevalence rates.
- To assess the utility of contraceptive logistics data for generating timely CPR estimates.
- To compare different modeling approaches for accuracy and feasibility.
Main Methods:
- Utilized Demographic and Health Surveys (DHS) and public-sector logistics data from 30 countries.
- Developed three models: couple-years of protection (CYP) conversion, bivariate linear regression, and multivariate linear regression.
- Evaluated models by comparing estimates to DHS prevalence rates using metrics like mean absolute error.
Main Results:
- Public-sector logistics data for oral contraceptives, injectable contraceptives, and male condoms correlated significantly with DHS prevalence rates.
- Regression models accurately estimated prevalence rates within 2 percentage points in at least 85% of countries.
- The CYP-based model for condoms showed weaker association compared to regression models.
Conclusions:
- Public-sector contraceptive logistics data are reliable for estimating prevalence of short-acting contraceptive methods.
- Developed models provide a valuable tool for generating interim CPR estimates.
- Regression models are most accurate, while the CYP model offers simplicity; further research on subnational data and other methods is recommended.
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